Recent years have witnessed the successful application of natural language generation.
Many of the applications in fact require certain degree of personalization, such as explainable recommendation [1]}, [2]}, [3]}, review generation [4]}, review summarization [5]}, and conversational systems [6]}, [7]}.
In these tasks, user and item IDs that distinguish one user/item from the others are crucial to personalization.
For example, in recommender systems, different users may care about different item features (e.g., style vs. quality), and different items may have different characteristics (e.g., fashionable vs. comfortable).
The goal of explainable recommendation [3]} is to provide an explanation to a user for a recommended item, so as to justify how the recommendation might match his/her interests.
That is, given a pair of user ID and item ID, the system needs to generate an explanation, such as “the style of the jacket is fashionable” (see the last column of Table REF  for more examples).
